{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 拼接训练集"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trainadd(name):\n",
    "    data = pd.read_csv(name)\n",
    "    csv_df = pd.DataFrame(data)\n",
    "    array = csv_df.to_numpy()\n",
    "    n=0\n",
    "    a,b,c = 0,0,0\n",
    "    for i in array[:,1:2]:\n",
    "        if i>0.3:\n",
    "            array[n,1]=1\n",
    "            a+=1\n",
    "        elif i>0:\n",
    "            array[n,1]=2\n",
    "            b+=1\n",
    "        else:\n",
    "            array[n,1]=3\n",
    "            c+=1\n",
    "        n+=1\n",
    "    df = pd.DataFrame(array)\n",
    "    colname = list(csv_df)\n",
    "    df.columns = colname\n",
    "    index_name = list(df.values[:,0])\n",
    "    #去掉第一行（\"公司\"）\n",
    "    df = df.drop(labels=\"公司\",axis = 1)\n",
    "    #将公司名改为Index\n",
    "    df.index = index_name\n",
    "    print(a,b,c)\n",
    "    return df\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pd.concat()将数据粘在一起，参数axis=？？？？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 3 8\n",
      "4 3 8\n",
      "5 1 9\n",
      "5 1 9\n",
      "5 2 8\n",
      "7 0 8\n",
      "7 0 8\n",
      "7 2 6\n",
      "7 3 5\n",
      "4 4 7\n",
      "5 2 8\n",
      "5 2 8\n",
      "5 3 7\n",
      "6 3 6\n",
      "7 2 6\n",
      "8 1 6\n",
      "8 1 6\n",
      "8 1 6\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>综合评分</th>\n",
       "      <th>总评名次</th>\n",
       "      <th>工人数</th>\n",
       "      <th>机器数</th>\n",
       "      <th>债券</th>\n",
       "      <th>工资系数</th>\n",
       "      <th>累计研发</th>\n",
       "      <th>累计分红</th>\n",
       "      <th>净资产</th>\n",
       "      <th>人均利润率</th>\n",
       "      <th>资本利润率</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>飞翔2号</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>我的小白</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>突击队</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>just_play</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>霸气外露</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>天涯之星</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YYY.W</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>湖北经济学院创新管理A队</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>卡布奇诺</th>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>伍零壹玖</th>\n",
       "      <td>3</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>270 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              综合评分  总评名次  工人数  机器数  债券  工资系数  累计研发  累计分红  净资产  人均利润率  资本利润率\n",
       "飞翔2号             1     1    1    1   7     6     3     1    4      4      2\n",
       "我的小白             1     2    1    1   4     2    10     3    1      1      1\n",
       "突击队              1     3    1    1   6     3     3     3    2      2      3\n",
       "just_play        1     4    1    1   2     4     9     3    3      3      4\n",
       "霸气外露             2     5    1    1   3     1     3     2    7      6      7\n",
       "...            ...   ...  ...  ...  ..   ...   ...   ...  ...    ...    ...\n",
       "天涯之星             3    10   12   12  12     8    12    10   10     10     10\n",
       "YYY.W            3    10   12   12  12     8    12    10   10     10     10\n",
       "湖北经济学院创新管理A队     3    10   12   12  12     8    12    10   10     10     10\n",
       "卡布奇诺             3    14   11   10  10     8     1    10   14     14     14\n",
       "伍零壹玖             3    15    1   11  11     8    11    10   15     15     15\n",
       "\n",
       "[270 rows x 11 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total = []\n",
    "for i in range(2,11):\n",
    "    file_name = str(i)+'03.csv'\n",
    "    df = trainadd(file_name)\n",
    "    total.append(df)\n",
    "for i in range(2,11):\n",
    "    file_name = 'D:/Desktop/新建文件夹 (3)/13334/'+str(i)+\"03.csv\"\n",
    "    df = trainadd(file_name)\n",
    "    total.append(df)\n",
    "res = pd.concat(total)\n",
    "res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = list(res.values[:,2:])\n",
    "y =list(res.values[:,0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 使用决策树"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "clf = tree.DecisionTreeClassifier(criterion='entropy',max_depth=3)\n",
    "clf = clf.fit(X,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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[91, 7, 0]</text>\n<text text-anchor=\"middle\" x=\"60.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n</g>\n<!-- 2&#45;&gt;3 -->\n<g id=\"edge3\" class=\"edge\">\n<title>2&#45;&gt;3</title>\n<path fill=\"none\" stroke=\"black\" d=\"M146.49,-103.73C134.62,-94.24 122,-84.16 110.19,-74.72\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"112.16,-71.81 102.16,-68.3 107.79,-77.28 112.16,-71.81\"/>\n</g>\n<!-- 4 -->\n<g id=\"node5\" class=\"node\">\n<title>4</title>\n<path fill=\"#9cf2c0\" stroke=\"black\" d=\"M243.5,-68C243.5,-68 151.5,-68 151.5,-68 145.5,-68 139.5,-62 139.5,-56 139.5,-56 139.5,-12 139.5,-12 139.5,-6 145.5,0 151.5,0 151.5,0 243.5,0 243.5,0 249.5,0 255.5,-6 255.5,-12 255.5,-12 255.5,-56 255.5,-56 255.5,-62 249.5,-68 243.5,-68\"/>\n<text text-anchor=\"middle\" x=\"197.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.918</text>\n<text text-anchor=\"middle\" x=\"197.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 9</text>\n<text text-anchor=\"middle\" x=\"197.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [3, 6, 0]</text>\n<text text-anchor=\"middle\" x=\"197.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 2</text>\n</g>\n<!-- 2&#45;&gt;4 -->\n<g id=\"edge4\" class=\"edge\">\n<title>2&#45;&gt;4</title>\n<path fill=\"none\" stroke=\"black\" d=\"M197.5,-103.73C197.5,-95.52 197.5,-86.86 197.5,-78.56\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"201,-78.3 197.5,-68.3 194,-78.3 201,-78.3\"/>\n</g>\n<!-- 6 -->\n<g id=\"node7\" class=\"node\">\n<title>6</title>\n<path fill=\"#f7dac5\" stroke=\"black\" d=\"M383,-68C383,-68 286,-68 286,-68 280,-68 274,-62 274,-56 274,-56 274,-12 274,-12 274,-6 280,0 286,0 286,0 383,0 383,0 389,0 395,-6 395,-12 395,-12 395,-56 395,-56 395,-62 389,-68 383,-68\"/>\n<text text-anchor=\"middle\" x=\"334.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.49</text>\n<text text-anchor=\"middle\" x=\"334.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 24</text>\n<text text-anchor=\"middle\" x=\"334.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [12, 7, 5]</text>\n<text text-anchor=\"middle\" x=\"334.5\" y=\"-7.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 1</text>\n</g>\n<!-- 5&#45;&gt;6 -->\n<g id=\"edge6\" class=\"edge\">\n<title>5&#45;&gt;6</title>\n<path fill=\"none\" stroke=\"black\" d=\"M374.67,-103.73C369.6,-95.06 364.25,-85.9 359.15,-77.18\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"362.03,-75.17 353.96,-68.3 355.99,-78.7 362.03,-75.17\"/>\n</g>\n<!-- 7 -->\n<g id=\"node8\" class=\"node\">\n<title>7</title>\n<path fill=\"#ede3fb\" stroke=\"black\" d=\"M517.5,-68C517.5,-68 425.5,-68 425.5,-68 419.5,-68 413.5,-62 413.5,-56 413.5,-56 413.5,-12 413.5,-12 413.5,-6 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class=\"node\">\n<title>9</title>\n<path fill=\"#f1e9fc\" stroke=\"black\" d=\"M708.5,-187C708.5,-187 616.5,-187 616.5,-187 610.5,-187 604.5,-181 604.5,-175 604.5,-175 604.5,-116 604.5,-116 604.5,-110 610.5,-104 616.5,-104 616.5,-104 708.5,-104 708.5,-104 714.5,-104 720.5,-110 720.5,-116 720.5,-116 720.5,-175 720.5,-175 720.5,-181 714.5,-187 708.5,-187\"/>\n<text text-anchor=\"middle\" x=\"662.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> 债券 &lt;= 8.5</text>\n<text text-anchor=\"middle\" x=\"662.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 1.272</text>\n<text text-anchor=\"middle\" x=\"662.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 16</text>\n<text text-anchor=\"middle\" x=\"662.5\" y=\"-126.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [1, 7, 8]</text>\n<text text-anchor=\"middle\" x=\"662.5\" y=\"-111.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">class = 3</text>\n</g>\n<!-- 8&#45;&gt;9 -->\n<g id=\"edge9\" class=\"edge\">\n<title>8&#45;&gt;9</title>\n<path fill=\"none\" stroke=\"black\" d=\"M662.5,-222.91C662.5,-214.65 662.5,-205.86 662.5,-197.3\"/>\n<polygon fill=\"black\" stroke=\"black\" points=\"666,-197.02 662.5,-187.02 659,-197.02 666,-197.02\"/>\n</g>\n<!-- 12 -->\n<g id=\"node13\" class=\"node\">\n<title>12</title>\n<path fill=\"#823be5\" stroke=\"black\" d=\"M935,-187C935,-187 808,-187 808,-187 802,-187 796,-181 796,-175 796,-175 796,-116 796,-116 796,-110 802,-104 808,-104 808,-104 935,-104 935,-104 941,-104 947,-110 947,-116 947,-116 947,-175 947,-175 947,-181 941,-187 935,-187\"/>\n<text text-anchor=\"middle\" x=\"871.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\"> 人均利润率 &lt;= 7.0</text>\n<text text-anchor=\"middle\" x=\"871.5\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.075</text>\n<text text-anchor=\"middle\" x=\"871.5\" 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      "text/plain": [
       "<graphviz.sources.Source at 0x269c3116c70>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import graphviz\n",
    "viewData = tree.export_graphviz(clf,\n",
    "feature_names = list(res)[2:],\n",
    "class_names= np.array([\"1\",\"2\",\"3\"],dtype=\"<U10\"),\n",
    "filled = True,\n",
    "rounded =True)\n",
    "graph = graphviz.Source(viewData)\n",
    "#graph.view()\n",
    "graph.render('clf_tree2') \n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读取新文件来测试"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trans(name):\n",
    "    data = pd.read_csv(name)\n",
    "    csv_df = pd.DataFrame(data)\n",
    "    array = csv_df.to_numpy()\n",
    "    n=0\n",
    "    for i in array[:,1:2]:\n",
    "        if i>0.3:\n",
    "            array[n,1]=1\n",
    "        elif i>0:\n",
    "            array[n,1]=2\n",
    "        else:\n",
    "            array[n,1]=3\n",
    "        n+=1\n",
    "    df = pd.DataFrame(array)\n",
    "    colname = list(csv_df)\n",
    "    df.columns = colname\n",
    "    index_name = list(df.values[:,0])\n",
    "    #去掉第一行（\"公司\"）\n",
    "    df = df.drop(labels=\"公司\",axis = 1)\n",
    "    #将公司名改为Index\n",
    "    df.index = index_name\n",
    "    X_test = list(df.values[:,2:])\n",
    "    y_test =list(df.values[:,0])\n",
    "    return X_test,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.8083333333333333\n",
      "[0.8, 0.8666666666666667, 0.8666666666666667, 0.6666666666666666, 0.7333333333333333, 0.7333333333333333, 0.8666666666666667, 0.9333333333333333]\n"
     ]
    }
   ],
   "source": [
    "scores = []\n",
    "for i in range(2,10):\n",
    "    file_name = 'D:/Desktop/新建文件夹 (3)/13332/'+str(i)+\"03.csv\"\n",
    "    X_test,y_test = trans(file_name)\n",
    "    score = clf.score(X_test,y_test)\n",
    "    scores.append(score)\n",
    "print(np.mean(scores))\n",
    "print(scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制学习曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对训练集评分\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.0, 0.8666666666666667, 0.9333333333333333, 0.9333333333333333, 1.0, 1.0]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = []\n",
    "for i in range(6):\n",
    "    clf = tree.DecisionTreeClassifier(criterion='entropy',max_depth=i+1)\n",
    "    clf = clf.fit(X,y)\n",
    "    scores = []\n",
    "    test.append(clf.score(X_test,y_test))\n",
    "print(test)\n",
    "plt.plot(range(1,7),test,color='red',label=\"max_depth\")\n",
    "plt.title('max_depth-score') \n",
    "plt.xlabel(\"Tree's Max Depth\")\n",
    "plt.ylabel(\"Model Score\")\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 对测试集评分"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.8583333333333334, 0.8666666666666667, 0.7916666666666667, 0.7916666666666667, 0.7666666666666667, 0.8166666666666667]\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "test = []\n",
    "\n",
    "for i in range(6):\n",
    "    clf = tree.DecisionTreeClassifier(criterion='entropy',max_depth=i+1)\n",
    "    clf = clf.fit(X,y)\n",
    "    scores = []\n",
    "    for i in range(2,10):\n",
    "        file_name = 'D:/Desktop/新建文件夹 (3)/13331/'+str(i)+\"03.csv\"\n",
    "        X_test,y_test = trans(file_name)\n",
    "        score = clf.score(X_test,y_test)\n",
    "        scores.append(score)\n",
    "    test.append(np.mean(scores))\n",
    "print(test)\n",
    "plt.plot(range(1,7),test,color='red',label=\"max_depth\")\n",
    "plt.legend()\n",
    "plt.title('gameid:13331 - max_depth - test_score') \n",
    "plt.xlabel(\"Tree's Max Depth\")\n",
    "plt.ylabel(\"Test Score\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "5203f191fb92a3c9962196e3c1c9bdaa035148bbce8d7674a8a57fbc91212ce7"
  },
  "kernelspec": {
   "display_name": "Python 3.8.9 64-bit",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.9"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
